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      <title>决策树特征选择 - 学习卡片</title>
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        <h1>决策树特征选择 - 学习卡片</h1>
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      <div class="card">
        <div class="card-face card-front">
          <div class="card-category">机制</div>
          <div class="card-question">在构建决策树时，选择最优分裂特征的核心策略是什么？</div>
          <div class="card-footer">点击卡片查看答案</div>
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        <div class="card-face card-back">
          <div class="card-category">机制</div>
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            <div class="card-answer">核心策略是使用信息增益（Information Gain）或基尼指数（Gini Index）来评估每个特征对分类的贡献，并优先选择信息增益最大的特征进行分裂。</div>
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          <div class="card-source">来源: 解题思路</div>
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    <div class="card-container" onclick="this.classList.toggle('flipped');">
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          <div class="card-category">理论</div>
          <div class="card-question">什么是信息增益（Information Gain），它的计算公式是怎样的？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">理论</div>
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            <div class="card-answer">信息增益是指数据集在根据某个特征分裂后，熵（不确定性）的减少量。其计算公式为：信息增益 = 分裂前的熵 - 分裂后的加权熵。</div>
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          <div class="card-source">来源: 示例代码（informationGain方法注释）</div>
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    <div class="card-container" onclick="this.classList.toggle('flipped');">
      <div class="card">
        <div class="card-face card-front">
          <div class="card-category">理论</div>
          <div class="card-question">文档中提到的熵（Entropy）的计算公式是什么？</div>
          <div class="card-footer">点击卡片查看答案</div>
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        <div class="card-face card-back">
          <div class="card-category">理论</div>
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            <div class="card-answer">熵的计算公式为 H = -Σ(p * log₂(p))，其中 p 是数据集中每个类别的概率。</div>
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          <div class="card-source">来源: 示例代码（entropy方法注释）</div>
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    <div class="card-container" onclick="this.classList.toggle('flipped');">
      <div class="card">
        <div class="card-face card-front">
          <div class="card-category">理论</div>
          <div class="card-question">文档中提到的基尼指数（Gini Index）的计算公式是什么？</div>
          <div class="card-footer">点击卡片查看答案</div>
        </div>
        <div class="card-face card-back">
          <div class="card-category">理论</div>
          <div class="card-answer-wrapper">
            <div class="card-answer">基尼指数的计算公式为 Gini = 1 - Σ(p²)，其中 p 是数据集中每个类别的概率。</div>
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          <div class="card-source">来源: 示例代码（giniIndex方法注释）</div>
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          <div class="card-category">技术</div>
          <div class="card-question">文档中描述的决策树特征选择算法的时间和空间复杂度分别是多少？</div>
          <div class="card-footer">点击卡片查看答案</div>
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        <div class="card-face card-back">
          <div class="card-category">技术</div>
          <div class="card-answer-wrapper">
            <div class="card-answer">时间复杂度为 O(n * m)，其中 n 为样本数，m 为特征数；空间复杂度为 O(m)。</div>
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          <div class="card-source">来源: 解题思路</div>
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    <div class="card-container" onclick="this.classList.toggle('flipped');">
      <div class="card">
        <div class="card-face card-front">
          <div class="card-category">机制</div>
          <div class="card-question">在计算信息增益时，分裂后的“加权熵”是如何计算的？</div>
          <div class="card-footer">点击卡片查看答案</div>
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        <div class="card-face card-back">
          <div class="card-category">机制</div>
          <div class="card-answer-wrapper">
            <div class="card-answer">首先，根据一个特征的所有可能取值将数据集划分为多个子集。然后，计算每个子集的熵，并乘以该子集占总数据集大小的比例（即权重）。最后，将所有子集的加权熵相加得到最终结果。</div>
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          <div class="card-source">来源: 示例代码（informationGain方法实现逻辑）</div>
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